Who is this presentation for?

Prerequisite knowledge

What you'll learn

Learn how to build distributed deep learning models on top of Spark and end-to-end analytics and deep learning applications

Understand how to visualize the training process with TensorBoard

Description

The rapid development of deep learning in recent years has greatly changed the landscape of data analytics and machine learning and helped empower the success of many applications for artificial intelligence. BigDL, a new distributed deep learning framework on Apache Spark, provides easy and seamlessly integrated big data and deep learning capabilities for users.

Yuhao Yang and Zhichao Li share real-world examples of end-to-end analytics and deep learning applications, such as speech recognition (e.g., Deep Speech 2), object detection (e.g., Single Shot Multibox Detector), and recommendations, on top of BigDL and Spark, with a particular focus on how the users leveraged the BigDL models, feature transformers, and Spark ML to build complete analytics pipelines. Yuhao and Zhichao also explore recent developments in BigDL, including full support for Python APIs (built on top of PySpark), notebook and TensorBoard support, TensorFlow model R/W support, better recurrent and recursive net support, and 3D image convolutions.

Yuhao Yang

Intel

Yuhao Yang is a senior software engineer on the big data team at Intel, where he focuses on deep learning algorithms and applications—particularly distributed deep learning and machine learning solutions for fraud detection, recommendation, speech recognition, and visual perception. He’s also an active contributor to Apache Spark MLlib.